Simulation method and simulator apparatus

ABSTRACT

A geometric model of an organ represents its shape as a collection of elements formed from nodes and connections among them. A first vessel network model represents a network of first vessels whose diameters are larger than or equal to a threshold. A plurality of second vessel networks each represent a network of second vessels whose diameters are smaller than the threshold. In a simulator apparatus, a first analysis unit analyzes hemodynamics in the first vessels, based on the geometric model and first vessel network model of the organ and reflecting the motion of the organ. A second analysis unit analyzes hemodynamics in the second vessel network models connected to the nodes, by using output data of the first analysis unit which indicates the hemodynamics in the first vessels at each of the nodes.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority of theprior Japanese Patent Application No. 2012-109067, filed on May 11,2012, the entire contents of which are incorporated herein by reference.

FIELD

The embodiments discussed herein relate to a simulation method and asimulator apparatus.

BACKGROUND

Recent advancement of measurement techniques in the biomedical field hasenabled the researchers to make a considerable number of findings inproteins, cells, and organs. From the viewpoint of measurement,proteins, cells, and organs are distinct objects with different relativescales. One measurement technique is only applicable to the objectswithin a particular scale range, and so are the findings of themeasurement. This means that the measurement-based approach is notcapable enough for the researchers to make a satisfactory finding in thestudy field of, for example, interactive behavior of cells and organs.The difficulty comes from the large difference in scale between thecells and organs.

The computer technology, on the other hand, have evolved rapidly towardever higher performance, and computer simulation takes advantage of suchperformance enhancements. Simulation-based research has beenincreasingly used to compensate for the lack of findings in the studythat deals with objects in different scales (e.g., large objects incombination with small objects). This approach appears to be leading tonew discoveries which could not have been achieved by the conventionalmeasurement-based or experiment-based study.

Computer simulation is used in research of the heart, one of the mostpopular organs as a subject of study. The heart is an important organthat sends out blood to the entire body, where the blood circulation isone of the most fundamental mechanisms of life. It is noted that theheart itself also needs a stable supply of blood. Delivery of blood tothe heart is done via a set of vessels known as the coronary circulatorysystem. Pathological conditions derived from abnormalities in thecoronary circulation are collectively referred to as ischemic heartdiseases.

Ischemic heart diseases are mainly caused as a result of coronaryarteriosclerosis. Most studies about arteriosclerosis are made from theviewpoint of the biochemistry and cell biology. The pathogenesis ofischemic heart diseases is closely related to functional factors such asstructure and control of the coronary circulatory system. For example,the heart contraction is considered to give a strong effect on thecoronary blood flow. It is known that, with the presence of anginapectoris, ischemia is likely to occur at the endocardial (heart chamber)side of the heart wall, where the tissue pressure increases mostsignificantly. The tissue pressure can be estimated by using a mechanicsanalysis tool. Observation of blood flow (shear stress) related toatheroma formation is also important in identifying the pathogenesis ofischemic heart diseases. The mechanics analysis also works well for thisblood flow observation. As can be seen, the mechanics analysis plays animportant role in study of the pathogenesis of ischemic heart diseases.

Such hemodynamics peculiar to the coronary circulation has drawn theinterest of researchers including those who engaged in the basic medicalsciences. They have made various kinds of experimental research and haveaccumulated many findings. There still remain, however, a lot of unknownthings about the heart because of its complexity in structure andmuscular motion. For example, the coronary system includes a wide rangeof vessels, from trunks to capillaries, on the order of mm to 1 μm indiameter. That is, the largest coronary vessels are about 1,000 times aswide as the smallest coronary vessels. In addition, the heart repeatscontraction and relaxation to make a large motion of strokes, which addsconstraints to realtime observation of coronary blood vessels. Thesefacts hamper the investigation of hemodynamics inside the heart wall.Computer simulation with a coronary circulation model is one way toovercome such technical difficulties in the physical observation ofcoronary vessels. This indeed is a powerful tool to illuminate themechanisms of the heart.

Databases for coronary circulation simulation are available today toprovide detailed measurement data about, for example, the diameter andlength of coronary arteries, veins, and capillaries, as well as theinformation describing how the vessels branch out. With such databases,various simulation methods have been proposed and discussed as listedbelow. A coronary circulation simulation includes computation forsolving simultaneous linear equations by using a parallel iterativemethod. For example, a preconditioner for parallel interactive sover isproposed to overcome the ill-conditioned problem caused by widelyranging vessel diameters in the coronary circulatory system and toensure the convergence of iteration.

G. S. Kassab, C. A. Rider, N. J. Tang and Y. C. Fung, “Morphometry ofPig Coronary Arterial Trees,” American Physiological Society Heart andCirculatory Physiology, 1993, 265: H350-H365.

G. S. Kassab, D. H. Lin and Y. C. Fung, “Morphometry of Pig CoronaryVenous System,” American Physiological Society Heart and CirculatoryPhysiology, 1994, 267: H2100-H2113.

G. S. Kassab and Y. C. Fung, “Topology and Dimensions of Pig CoronaryCapillary Network,” American Physiological Society Heart and CirculatoryPhysiology, 1994, 267: H319-H325.

Washio, Okada, Hisada, “A Parallel Preconditioning for Heart-CoronaryCirculation Coupling Simulation,” JSCES Conference Proceedings, TheJapan Society for Computational Engineering and Science (JSCES), May2009, Vol. 14

Cortassa S, Aon M A, O'Rourke B, Jacques R, Tseng H-J, Marban E, WinslowR L, “A computational model Integrating electrophysiology, contraction,and mitochondrial bioenergetics in the ventricular myocyte,” Biophys J91, pp. 1564-1589, 2006

The existing simulation techniques are, however, unable to analyze thedynamics of a circulatory system that not only involves a wide range ofvessels, from coronary arteries and veins to capillaries, but alsoincorporates the motion of heartbeats. The main reason is that themodeling of capillaries, if done faithfully, would produce too largeamount of data for the simulator to finish its job within a practicaltime. One way to circumvent the noted problem is to use a porous mediumas a substitute for the capillary-level vessels. This conventionalsolution, however, sacrifices the ability to analyze the pressure dropsin capillaries, effect of a microcirculation structure on the coronaryblood flow, or interaction between metabolism and microcirculation. Herethe term “metabolism” refers to a process in myocardial cells thatconsumes oxygen and nutrition delivered by the capillary blood flow toproduce adenosine triphosphate (ATP) for energizing the cells.

The above-noted difficulties in the simulation of coronary circulationmay also be true with other organs than the heart because their bloodflow varies in synchronization with their motions. This means that thenoted difficulties in the dynamics analysis of a circulatory systemsimilarly apply to the conventional simulation of other organs becauseof the variety of vessels from trunks to capillaries and the effect oftheir motions on the vessels.

SUMMARY

According to an aspect of the embodiments to be discussed herein, thereis provided a simulation method performed by a processor. Thissimulation method includes: storing a geometric model representing ashape of an organ as a collection of elements formed from a plurality ofnodes and a plurality of connections among the nodes; storing a firstvessel network model representing a network of first vessels whosediameters are larger than or equal to a specified threshold; storing aplurality of second vessel networks each representing a network ofsecond vessels whose diameters are smaller than the specified threshold,wherein one or more of the second vessel networks are connected to eachof the nodes; first analyzing, by a processor, hemodynamics in the firstvessels belonging to the first vessel network model, based on thegeometric model of the organ and reflecting motion of the organ; andsecond analyzing, by the processor, hemodynamics in the second vesselsbelonging to the second vessel network models connected to the pluralityof nodes, by using result data of the first analyzing which indicatesthe hemodynamics in the first vessels at each of the nodes.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an exemplary functional structure of a parallelcomputing system according to a first embodiment;

FIG. 2 illustrates an example of vessel network models representinglarge coronary arteries and veins;

FIG. 3 illustrates an example of a microcirculation model;

FIG. 4 illustrates an example of a symmetrical model;

FIG. 5 illustrates an example of a micro model;

FIG. 6 illustrates how a macro model is connected to micro models via apressure node;

FIG. 7 depicts an example of a bifurcated vessel model;

FIG. 8 illustrates an exemplary system configuration according to asecond embodiment;

FIG. 9 depicts an exemplary hardware structure of a CAD system;

FIG. 10 depicts an exemplary hardware configuration of a computing node;

FIG. 11 is a functional block diagram of devices according to the secondembodiment;

FIG. 12 illustrates an exemplary data structure of a finite elementmodel;

FIG. 13 illustrates an exemplary data structure of a macro model;

FIG. 14 illustrates an exemplary data structure of a micro model;

FIG. 15 is a flowchart illustrating an exemplary procedure of a coronarycirculation simulation;

FIG. 16 illustrates exemplary classification of vessels in a coronarycirculation model;

FIG. 17 is a flowchart illustrating an example of a macro modelingprocess;

FIG. 18 illustrates an exemplary view of an epicardial coronary vesselmodel;

FIG. 19 illustrates an example of an epicardial coronary circulationmodel with septal branches;

FIG. 20 is a flowchart illustrating an example of a micro modelingprocess;

FIG. 21 depicts an exemplary procedure of building a microcirculationmodel;

FIG. 22 illustrates an exemplary micro model in which 128microcirculation models are connected in parallel at end points of asymmetrical model;

FIG. 23 illustrates a distribution of relative vessel density;

FIG. 24 illustrates a difference of vessel diameters;

FIG. 25 is a flowchart illustrating an exemplary procedure of a coronarycirculation simulation;

FIG. 26 is a flowchart illustrating an exemplary simulation procedureaccording to a fourth embodiment; and

FIG. 27 illustrates an exemplary system configuration according to afifth embodiment.

DESCRIPTION OF EMBODIMENTS

Several embodiments will be described below with reference to theaccompanying drawings, wherein like reference numerals refer to likeelements throughout. Each of those embodiments may be combined withother embodiments as long as there are no contradictions between them.

(A) First Embodiment

This section describes a simulation method according to a firstembodiment, which improves the speed of computation relative to theconventional methods by applying a distributed processing scheme for amulti-scale analysis while taking advantage of anatomical features oforgans under analysis. For example, the heart has an anatomical featurethat there are no direct connections within the microcirculatory system,while the vessels are joined together only via some branches in theirupstream or downstream portion. This anatomical feature of the heart maybe utilized to improve the speed of simulation. Further, the firstembodiment assumes symmetry of medium-scale vessels betweenmicrocirculatory vessels and large vessels. This assumption enablessubstantial reduction in the amount of computation.

FIG. 1 illustrates an exemplary functional structure of a parallelcomputing system according to the first embodiment. The illustratedsimulator apparatus X includes a storage unit 1 and a plurality ofcomputing devices 2, 3, 4, 5, and so on. This simulator apparatus Xoperates as a parallel computing system by executing concurrentprocesses on the computing devices 2, 3, 4, 5, . . . in parallel.

The storage unit 1 stores a geometric model 1 a, a first vessel networkmodel 1 b, and a second vessel network model 1 c. The geometric model 1a is a collection of geometric data that defines the shape of an organas a plurality of elements (or interconnected nodes). The first vesselnetwork model 1 b is a collection of data that defines a network ofintraorgan vessels whose diameters are greater than or equal to aspecified threshold. The second vessel network model 1 c is a collectionof data that defines a network of intraorgan vessels whose diameters aresmaller than the threshold. In the case of heart simulation, the firstand second vessel network models 1 b and 1 c represent coronarycirculation networks of the heart under analysis.

One computing device 2 includes a first analyzing unit 2 a. Based on thegeometric model 1 a and first vessel network model 1 b, the firstanalyzing unit 2 a analyzes the condition of blood in the vessels of thefirst vessel network model 1 b. The result data of this analysisindicates blood pressure and other properties at a plurality of nodes ofthe geometric model 1 a. The organ under analysis may move as in theheartbeat caused by myocardial contraction and relaxation. Such motionof the organ could directly affect the second vessel network model 1 c.The first analyzing unit 2 a analyzes the state of several physicalquantities such as pressure at each node by, for example, combining ablood flow analysis and an organic motion analysis of the first vesselnetwork model 1 b.

The above-described first analyzing unit 2 a is employed in onecomputing device 2, while the other computing devices 3, 4, 5, . . .include second analyzing units 3 a, 4 a, 5 a, . . . , respectively. Withthe information on hemodynamics at a plurality of nodes, the secondanalyzing units 3 a, 4 a, 5 a, . . . analyze the hemodynamics in thesecond vessel network models 1 c coupled to those nodes. For example,one or more second vessel network models 1 c are coupled to each node ofthe geometric model 1 a, and these second vessel network models 1 c areassigned to the second analyzing units 3 a, 4 a, 5 a, distributedly. Thesecond analyzing units 3 a, 4 a, 5 a, . . . analyze the hemodynamics intheir assigned second vessel network models 1 c by using data ofpressure at each node of those second vessel networks models 1 c. Inthis way, many computing devices 3, 4, 5, . . . operate in parallel toanalyze the hemodynamics in a plurality of micro models.

The above computing devices 2, 3, 4, 5, . . . may each be implemented asa central processing unit (CPU) in the simulator apparatus X. These CPUsmay be multi-core CPUs, in which case the individual cores in each CPUmay serve as the computing devices discussed above. The storage unit 1,on the other hand, may be implemented as part of random access memory(RAM), hard disk drives (HDD), or other storage media in the simulatorapparatus X.

It is noted that the lines interconnecting the functional blocks in FIG.1 are only an example. FIG. 1 omits some communication paths forsimplicity purposes. The person skilled in the art would appreciate thatthere may be other communication paths in the actual implementations.

The following section will describe in detail how the above simulatorapparatus X of the first embodiment works in a coronary circulationsimulation as an example of hemodynamics simulation. In this context,the description uses the terms “macro model” and “micro model” to referto the first vessel network model 1 b and second vessel network model 1c, respectively. It is also assumed that the second analyzing units 3 a,4 a, 5 a, analyze hemodynamics in micro models by using data about nodepressures. The description uses the term “pressure node” to refer to anode to which a micro model(s) is coupled. More special terms aredefined below.

(a) Terms

-   -   large coronary artery, large coronary vein: vessels whose        diameter is greater than about 100 μm (see FIG. 2)    -   medium scale vessels: vessels whose diameters are in the range        of about 20 to 100 μm (see FIG. 4)    -   arteriole, venule: small vessels whose diameters are in the        range of about 10 to 20 μm (see FIG. 3)    -   capillary: a minute vessel with a diameter of about 5 to 7 μm,        whose wall acts as a membrane for interchange of oxygen and        various nutrients between the blood and myocardial cells (see        FIG. 3)    -   microcirculatory system: a system formed from arterioles,        capillaries, and venules for delivering oxygen and various        nutrients to myocardial cells (see FIG. 3)

(b) Multi-Scale Vessel Network Modeling

The first embodiment deals with blood vessel networks in every scale,from large coronary arteries and veins to capillaries. In thismulti-scale blood vessel network, several large coronary arteriesoriginate from the aorta and branch out into smaller arteries, then intoarterioles, and finally into capillaries. The capillaries join and widento become venules and then to become veins. These veins further jointogether to form large coronary veins and finally reach the right atriumof the heart. Suppose that, at each branching point of vessels, a largervessel branch out into smaller vessels. The term “upper-scale vessel”will now be used to refer to the larger vessel. The term “lower-scalevessel” will be used to refer to the smaller vessels.

The process of modeling such a blood vessel network begins withproducing two separate network models, one for large coronary arteriesand the other for large coronary veins. FIG. 2 illustrates an example ofvessel network models representing large coronary arteries and veins.Seen in the left half of FIG. 2 is a network model 11 of large coronaryarteries, and seen in the right half is a network model 12 of largecoronary veins. These two network models 11 and 12 then form a macromodel 10.

The modeling process also provides a microcirculation model on the basisof anatomical data to represent a microcirculatory system includingarterioles, venules and capillaries. FIG. 3 illustrates an example ofsuch a microcirculation model. The illustrated microcirculation model 20of FIG. 3 includes two sets of blood vessel networks, one for arterioles21 (left) and the other for venules 22 (right). These two sets ofvessels are interconnected by yet another blood vessel network made upof capillaries 23.

The modeling process further produces more vessel network models of amedium scale to provide connections between the above microcirculationmodel 20 and macro model 10. One such blood vessel network model belongsto the arterial system, and the other belongs to the venous system. Itis assumed in the first embodiment that these two intermediate vesselnetworks are in symmetry with each other. Because of this assumption,the term “symmetric model” is used to refer to a pair of such vesselnetwork models lying between a macro model and a microcirculation model.

FIG. 4 illustrates an example of a symmetrical model. The illustratedsymmetric model 30 includes an intermediate arterial model 31 and anintermediate venous model 32, located on opposite sides of thecapillaries. As seen from FIG. 4, the intermediate venous model 32 is amirror image of the intermediate arterial model 31. In other words,these two intermediate models are similar to each other in the way ofbifurcation of upper-scale vessels into lower-scale vessels, as well asin the diameters and lengths of such branching vessels. The assumptionof symmetry enables drastic reduction of computational load, withoutlosing inherent characteristics of the blood vessel networks.

The upper-scale ends of the produced symmetric model 30 are coupled tothe macro model 10, while the lower-scale ends of the same are coupledto the microcirculation models 20. The symmetric model 30 andmicrocirculation models 20 form a vessel network model called a “micromodel.”

FIG. 5 illustrates an example of a micro model. The illustrated micromodel 40 includes an intermediate arterial model 31, an intermediatevenous model 32, and a plurality of microcirculation models 20 a, . . ., 20 n.

The modeling process now joins the macro model 10 and micro model 40together, assuming that they virtually meet at the same points aspressure nodes k of the cardiac muscle. Pressure nodes k are vertices ofeach polyhedral element constituting a finite element model of theheart. The micro model 40 receives an external pressure from the cardiacmuscle at each pressure node k, which is referred to as a myocardialnode pressure P_(k).

The number of micro models 40 coupled to a pressure node k is determinedfrom lumped myocardial node volume V_(k) ^(muscle) of that pressurenode. That is, the volume of each element is distributed to the pressurenodes that constitute the element. The term “lumped myocardial nodevolume” or V_(k) ^(muscle) refers to the sum of those distributedvolumes at a specific pressure node k. For example, the elements may betetrahedrons, each having four pressure nodes. In this case, the volumeof one tetrahedron element is divided into four parts, and the dividedvolumes are each assigned to the four pressure nodes as their lumpedmyocardial node volumes V_(k) ^(muscle). One pressure node k may beshared by m elements, where m is an integer greater than zero. Then thelumped myocardial node volume V_(k) ^(muscle) at that node k iscalculated by adding up the contributions of volume from those melements.

The heart is known to have a higher density of blood vessels inside thanoutside. Specifically, the density of small vessels per myocardialvolume varies depending on the transmurality (i.e., depth from the outerwall of the heart). Based on this anatomical feature of the heart, themodeling process takes the transmural difference into consideration whendetermining how many micro models 40 to connect to a pressure nodes k.For example, the lumped myocardial node volume V_(k) ^(muscle) of apressure node is to be multiplied by a relative vessel density thatreflects the transmurality of the node. More specifically, the relativevessel density increases as the pressure node is closer to the inside ofthe heart.

The modeling process then calculates a micro model vessel capacity ρ_(k)representing the capacity of blood vessels contained in a unit volume ateach pressure node. This calculation is performed on the basis of theratio of total volume of vessels in the micro models to the myocardialvolume. Let V^(micro) be the total capacity of blood vessels in a singlemicro model. Then the following equation gives the number n_(k) of micromodels connected to a pressure node k at an extreme end point of themacro model.

$\begin{matrix}{n_{k} = \frac{V_{k}^{muscle}\rho_{k}}{V^{micro}}} & (1)\end{matrix}$

Now that the number n_(k) of connected micro models is determined foreach of the pressure nodes, the following flow rate conservation lawapplies to each junction of arterial vessels and venous vessels in themacro model and micro models.

Q _(macro) ^(γ) n _(k) Q _(micro) ^(γ)=0  (2)

where Q_(macro) ^(γ) represents the flow rate of blood sent out from amacro model to micro models, Q_(micro) ^(γ) represents the flow rate ofblood sent out from a micro model to a macro model, and γ is a symbolfor distinguishing arterial vessels from venous vessels.

FIG. 6 illustrates how a macro model is connected to micro models via apressure node. The illustrated macro model 10 particularly represents ablood vessel network that belongs to the arterial system. The personskilled in the art would appreciate that FIG. 6 similarly applies to thevenous side of this simulation model as well. Seen in the center of FIG.6 is an element of the finite element model, in the form of a polyhedronobtained by placing line segments to interconnect adjacent pressurenodes. This element 51 includes a pressure node k via which a pluralityn_(k) of micro models 40 a are connected to the nearest vessel of themacro model 10.

On the arterial side, the blood flows from the macro model to aplurality n_(k) of micro models 40 via a pressure node k. The macromodel flow rate Q_(macro) ^(γ) is thus divided into n_(k). On the venousside, the blood flows from n_(k) micro models 40 into a vessel of themacro model via a pressure node. The micro model flow rates Q_(micro)^(γ) of these n_(k) flows are combined into one. The direction of amicro model blood flow is expressed in the sign of Q_(micro) ^(γ)Specifically, Q_(micro) ^(γ) takes a negative value when it represents ablood flow from a macro model 10 to micro models 40 as depicted in FIG.6. Accordingly, the sum of the macro flow Q_(macro) ^(γ) and n_(k) timesthe micro flow Q_(micro) ^(γ) is always zero as seen in equation (2).

(c) Computation Algorithms

This section describes computation algorithms used in the proposedcoronary circulation simulation. For exemplary purposes, the followingdescription assumes the use of a Newton-Raphson method for nonlinearanalysis. According to the first embodiment, the problem is solved byusing parallel computation based on the concept of multi-scale analysis.The entire system is given by the following matrix equation (3).

$\begin{matrix}{{\begin{pmatrix}A_{Mic} & C^{T} \\C & B_{Mac}\end{pmatrix}\begin{pmatrix}{\Delta \; \mu_{Mic}} \\{\Delta \; \mu_{Mac}}\end{pmatrix}} = \begin{pmatrix}r_{Mic} \\r_{Mac}\end{pmatrix}} & (3)\end{matrix}$

A_(Mic) is a coefficient matrix produced from a micro model of vessels.B_(Mac) is a coefficient matrix produced from a macro model of vessels.C is a matrix representing connections between the micro portion and themacro portion. C^(T) denotes the transposed matrix of C. Δμ_(Mic)represents increments of micro-model pressure in the nonlinear analysis,while Δμ_(Mac) is the same for macro model pressure. Lastly, r_(Mic)represents residuals produced in the nonlinear analysis of the micromodel, while r_(Mac) is the same for the macro model.

The coefficient matrix in equation (3) is broken into a product of twomatrices as follows.

$\begin{matrix}{{\begin{pmatrix}A_{Mic} & C^{T} \\C & B_{Mac}\end{pmatrix} = {\begin{pmatrix}A_{Mic} & 0 \\C & {\overset{\sim}{B}}_{Mac}\end{pmatrix}\begin{pmatrix}I & {A_{Mic}^{- 1}C^{T}} \\0 & I\end{pmatrix}}}{{\overset{\sim}{B}}_{Mac} = {B_{Mac} - {{CA}_{Mic}^{- 1}C^{T}}}}} & (4)\end{matrix}$

Based on this equation (4), the following three steps yield pressureincrements in the micro and macro models.

1. Calculate Δ{tilde over (μ)}_(Mic) from A _(Mic)Δ{tilde over(μ)}_(Mic) =r _(Mic)  (5)

2. Calculate Δμ_(Mac) from {tilde over (B)}Δμ _(Mac) =r _(Mac) −CΔ{tildeover (μ)} _(Mic)  (6)

3. Calculate Δμ_(Mic) from Δμ_(Mic)=Δ{tilde over (μ)}_(Mic) −A _(Mic) ⁻¹C ^(T)Δμ_(Mac).  (7)

The first step seen in equation (5) converts the micro model equationinto another form that eliminates the increments of macro-modelpressure. This calculation yields tentative pressure increments Δμ_(Mic)in the micro models. (Note that there is a tilde on top of μ.) Thesecond step seen in equation (6) then calculates pressure increments inthe macro model by using the tentative pressure increments obtained fromthe micro models. The third step seen in equation (7) uses thesepressure increments of the macro model to calculate true pressureincrements that the micro models are supposed to have.

The above steps remove the degrees of freedom of micro models so as notto intrude the macro model matrix, thus making it possible to separatemicro model calculation from macro model calculation. Here the micromodels are perfectly independent of each other. For this reason, theparallel processors can process their assigned micro models withoutinteractions, thus achieving efficient computation of coronarycirculation.

(d) Use of Symmetry to Reduce Computational Load

As discussed previously, the first embodiment uses a symmetric model 30for modeling medium-scale vessels in the intermediate layer thatconnects a macro model with capillaries. The use of such a symmetricmodel reduces the amount of computation. The following sectiondemonstrates how the first embodiment takes advantage of the symmetry,by using the simplest example, i.e., a bifurcated vessel formed fromthree elements and four nodes.

FIG. 7 depicts an example of a bifurcated vessel model. The illustratedbifurcated vessel model 41 represents a part of the arterial system.Specifically, this bifurcated vessel model 41 divides one upper-scalevessel into two lower-scale vessels. The numerals placed beside each endof these vessels are node numbers. Suppose now that blood flows into theupper-scale vessel at a rate of Q₁ while blood flows out of the twolower-scale vessels at respective rates of Q₂ and Q₃.

The conductance of each vessel is defined as follows. Here theconductance indicates how easily the blood passes, and its inversequantity is resistance. The higher the conductance of vessels is, theeasier the blood can flow through them. The symbol k_(ij) is used forthe conductance of a vessel section between the ith node and the jthnode, where i and j are integers greater than zero. For example, k₁₂represents the conductance of a vessel between node #1 and node #2.

The blood flow in the illustrated bifurcated vessel is expressed belowin equation (8), under the simplified condition that the flow isdetermined solely from the vessel conductance.

$\begin{matrix}{{{\sum\limits_{j \in F_{i}}{{k_{ij}(t)}\left( {\mu_{i} - \mu_{j}} \right)}} + {\sum\limits_{j \in F_{i}}\frac{{V_{i}(t)}}{t}}} = 0} & (8)\end{matrix}$

Based on this equation (8), a stiffness matrix is produced as follows.

$\begin{matrix}{{\begin{bmatrix}k_{12} & {- k_{12}} & 0 & 0 \\{- k_{12}} & {k_{12} + k_{23} + k_{24}} & {- k_{23}} & {- k_{24}} \\0 & {- k_{23}} & {- k_{23}} & 0 \\0 & {- k_{24}} & 0 & k_{24}\end{bmatrix}\begin{Bmatrix}\mu_{1} \\\mu_{2} \\\mu_{3} \\\mu_{4}\end{Bmatrix}} = \begin{Bmatrix}Q_{1} \\0 \\Q_{3} \\Q_{4}\end{Bmatrix}} & (9)\end{matrix}$

In these equations (8) and (9), Q_(i) is the flow rate of blood intonode #i, F_(i) represents a set of nodes that connect to node #i, μ_(i)denotes the blood pressure at node #i, and V is the volume of a vesselpassage.

The two lower-scale vessels branching from the upper-scale vessel aresupposed to be exactly symmetrical in terms of the diameter and length,as well as in the way of forming subsequent branches. This symmetry ofvessels leads to:

k ₂₃ =k ₂₄

μ₃=μ₄

Q ₃ =Q ₄

The matrix equation is therefore reduced as follows.

$\begin{matrix}{{\begin{bmatrix}k_{12} & {- k_{12}} & 0 \\{- k_{12}} & {k_{12} + {2\; k_{23}}} & {{- 2}\; k_{23}} \\0 & {{- 2}\; k_{23}} & {2\; k_{23}}\end{bmatrix}\begin{Bmatrix}\mu_{1} \\\mu_{2} \\\mu_{3}\end{Bmatrix}} = \begin{Bmatrix}Q_{1} \\0 \\{2\; Q_{3}}\end{Bmatrix}} & (10)\end{matrix}$

With this reduction, equation (10) is solved with a smaller amount ofcomputation.

As can be seen from the above explanation, the first embodimentimplements a multi-scale analysis by taking advantage of anatomicalfeatures of the blood vessel network. That is, micro models 40 aredefined on the basis of the feature that their microcirculatory systemsare connected together only via larger vessels. These micro models 40are connected to a macro model 10 at each node of the geometric model.The proposed method reduces the amount of data to be processed in theanalysis because a microcirculation model 20 including capillaries canbe analyzed with a unified structure of micro models 40. In addition,connections between macro models 10 and micro models 40 are allowed onlyat the pressure nodes. This restriction also contributes to a reducedprocessing load because of the fewer choices for locations provided froma plurality of second analyzing units 3 a, 4 a, 5 a, to the firstanalyzing unit 2 a. Parallel processing techniques can be used toanalyze micro models connected to many nodes because they areindependent of each other.

The above-described first embodiment provides a computation algorithmsuitable for parallel processing to reduce the simulation time. Unlikethe conventional circulatory simulation, the first embodiment reproducesthe structure of vessels down to the capillary level, besides making itpossible to simulate the beating of cardiac muscle. The first embodimentalso reduces the amount of computational operations and thus enhancesthe simulation speed, by taking advantage of the symmetry ofmedium-scale vessels extending between microcirculatory vessels andlarge vessels.

(B) Second Embodiment

This section describes a second embodiment which executes a coronarycirculation simulation with a parallel computing system formed from aplurality of computing nodes each having one or more multi-core CPUs.

FIG. 8 illustrates an exemplary system configuration according to thesecond embodiment. The second embodiment includes a computer-aideddesign (CAD) system 100 for the purpose of modeling a three-dimensionalheart structure and coronary vessel network, through the userinteraction. The CAD system 100 is linked to a managing node 200 via anetwork 61. The CAD system 100 sends the created models ofthree-dimensional heart structure and coronary vessel network to themanaging node 200, along with an execution command for a coronarycirculation simulation based on those models.

The managing node 200, on the other hand, is connected to a storagedevice 300 and a plurality of computing nodes 400, 500, 600, . . . viaanother network 62. The managing node 200 uses the storage device 300 tostore data of the above-noted models supplied from the CAD system 100.When an execution command requesting a coronary circulation simulationis received from the CAD system 100, the managing node 200 submits jobsto each computing node 400, 500, 600, . . . to initiate the requestedsimulation. This job submission processing includes, for example,sending various files and parameters from the managing node 200 tocomputing nodes, such as information about of storage locations of modeldata, and programs and parameters for use in the jobs.

The storage device 300 stores data of the models that the user hascreated with the CAD system 100. The storage device 300 includes aplurality of hard disk drives or solid state drives (SSD). These drivesmay be configured with the Redundant Array of Independent Disks (RAID)technology.

The computing nodes 400, 500, 600, . . . run in parallel to execute acoronary circulation simulation in accordance with commands from themanaging node 200. Since multi-core CPUs are used in these computingnodes 400, 500, 600, . . . each individual node is capable of executingmultiple processes concurrently.

FIG. 9 depicts an exemplary hardware structure of a CAD system. Theillustrated CAD system 100 includes a CPU 101 to control its entireoperation. The CPU 101 is connected to a RAM 102 and other variousdevices and interfaces on a bus 109. While FIG. 9 illustrates a singleCPU configuration, the CAD system 100 may have two or more such CPUs.When that is the case, the CPUs work in a coordinated way to control theentire system.

The RAM 102 serves as primary storage of the CAD system 100.Specifically, the RAM 102 is used to store at least some of theoperating system (OS) programs and application programs that the CPU 101executes, in addition to other various data objects that it manipulatesat runtime.

Other devices on the bus 109 include an HDD 103, a graphics processor104, an input device interface 105, an optical disc drive 106, aperipheral device interface 107, and a network interface 108. The HDD103 writes and reads data magnetically on its internal platters. The HDD103 serves as secondary storage of the CAD system 100 to store programand data files relating to the operating system and applications. Flashmemory and other semiconductor memory devices may also be used assecondary storage, similarly to the HDD 103.

The graphics processor 104, coupled to a monitor 63, produces videoimages in accordance with drawing commands from the CPU 101 and displaysthem on a screen of the monitor 63. The monitor 63 may be, for example,a cathode ray tube (CRT) display or a liquid crystal display.

The input device interface 105 is used to connect input devices such asa keyboard 64 and a mouse 65 and supply signals from these devices tothe CPU 101. The mouse 65 is a pointing device, which may be replacedwith other kinds of pointing devices such as touchscreen, tablet,touchpad, and trackball.

The optical disc drive 106 reads out data encoded on an optical disc 66,by using laser light. The optical disc 66 is a portable data storagemedium, the data recorded on which can be read as a reflection of lightor the lack of the same. The optical disc 66 may be a digital versatiledisc (DVD), DVD-RAM, compact disc read-only memory (CD-ROM),CD-Recordable (CD-R), or CD-Rewritable (CD-RW), for example.

The peripheral device interface 107 is a communication interface toattach peripheral devices to the CAD system 100. For example, theperipheral device interface 107 may be used to connect a memory device67 and a memory card reader/writer 68. The memory device 67 is a datastorage medium with a capability to communicate with the peripheraldevice interface 107. The memory card reader/writer 68 is an adapterused to write data to or read data from a memory card 69, which is adata storage medium in the form of a small card. The network interface108 is connected to a network 61 to exchange data with other computersor network devices (not illustrated).

FIG. 10 depicts an exemplary hardware configuration of a computing node.The illustrated computing node 400 includes a CPU 410, a memory 420, andan interconnect circuit 430. The CPU 410 is a multi-core processorcontaining a plurality of processor cores 411 to 418 capable ofexecuting multiple processes in parallel. The memory 420 stores programsthat the CPU 410 executes, as well as various data that the CPU 410needs to execute processing. For example, the memory 420 serves as aprimary storage device to store programs for coronary circulationsimulation. The interconnect circuit 430 is a communication circuitdesigned for high-speed optical communication. The interconnect circuit430 is used to exchange data with other computing nodes 400, 500, 600, .. . via a network 62. The CPU 410 uses this interconnect circuit 430 toread data of a cardiac model from the storage device 300 via the network62.

The above hardware configuration serves as a platform of processingfunctions to implement the second embodiment. That is, the CAD system100, managing node 200, and computing nodes 400, 500, 600, . . . realizeprocessing functions of the second embodiment by executing softwareprograms, which may be stored in various computer-readable storagemedia. For example, these programs may be provided in HDDs. In each ofthe CAD system 100, managing node 200, and computing nodes 400, 500,600, . . . , the CPU loads its local RAM with at least part of theprograms stored in the HDD and executes them on the RAM. The programsfiles may be stored in portable storage media such as optical discs 66,memory devices 67, and memory cards 69. It is noted that thecomputer-readable storage media for those programs do not includetransitory propagating signals.

Portable storage media, such as optical discs 66, memory devices 67, andmemory cards 69, are used to distribute program products for sale. Asanother form of product distribution, the program files may be stored ina computer storage device, so that the CAD system 100, managing node200, or computing nodes 400, 500, 600, . . . can download them from aserver computer via the network 61. In this case, the CAD system 100,managing node 200, or computing nodes 400, 500, 600, . . . store thedownloaded programs in their local RAM or HDD.

While the above description of FIGS. 8 to 10 has been directed to thesecond embodiment, the same hardware configurations may similarly applyto the foregoing simulator apparatus X of the first embodiment.

According to the second embodiment, the constituent components of thesystem provide their own functions as will be described below. FIG. 11is a functional block diagram of components that constitute the secondembodiment. The illustrated CAD system 100 includes a finite elementmodeling unit 110, a coronary circulation modeling unit 120, and asimulation condition determination unit 130. The finite element modelingunit 110 produces a finite element model representing the solid geometryof a heart, according to commands from the user. This finite elementmodel may be organized as a three-dimensional mesh of, for example,tetrahedrons. The tetrahedral mesh has been mentioned in the firstembodiment as an example of polyhedron elements.

According to commands from the user, the coronary circulation modelingunit 120 produces a coronary circulation model representing a bloodvessel network of a heart. Also according to commands from the user, thesimulation condition determination unit 130 determines under whatconditions the coronary circulation simulation is to be executed. Forexample, the simulation condition determination unit 130 determines howmany micro models to connect to each pressure node of the cardiacmuscle.

The managing node 200 includes an execution command unit 210 configuredto command a plurality of computing nodes 400, 500, 600, . . . toexecute a coronary circulation simulation with the finite element modeland coronary circulation model produced by using the CAD system 100. Forexample, the execution command unit 210 receives these two models fromthe CAD system 100 and stores them in a storage device 300. Theexecution command unit 210 then selects computing nodes to submit jobsof the coronary circulation simulation. For example, the executioncommand unit 210 may select every computing node that has no jobs toexecute at the moment. In the case where a specific degree ofparallelism is designated previously for the coronary circulationsimulation, the execution command unit 210 selects as many computingnodes as needed to achieve the designated parallelism. The selectedcomputing nodes are supposed to receive a job assignment. The executioncommand unit 210 further singles out one computing node for macroanalysis while assigning the remaining computing nodes for microanalysis. The execution command unit 210 submits a job to the formercomputing node (e.g., the first computing node 400 in FIG. 11) toperform a macro analysis, as well as to control the entire process ofsimulation. The execution command unit 210 also submits jobs to theremaining computing nodes (e.g., the second, third, and subsequentcomputing nodes 500, 600, . . . in FIG. 11) to perform a micro analysis.

The storage device 300 stores a finite element model 310, a macro model320, and a micro model 330. The finite element model 310 is an exampleof the geometric model 1 a discussed in the first embodiment of FIG. 1.The macro model 320 is an example of the first vessel network model 1 bdiscussed in the same. The micro model 330 is an example of the secondvessel network model 1 c discussed in the same. A coronary circulationmodel is formed by combining the macro model 320 and micro model 330.

The first computing node 400 includes a simulation control unit 440 anda macro analysis unit 450. The simulation control unit 440 controls theentire process of a coronary circulation simulation. The macro analysisunit 450 undertakes a macro-modeling part of the coronary circulationsimulation, such as the analysis of heart beats produced by myocardialcontraction and relaxation and the blood flows in large coronaryarteries and veins. The simulation control unit 440 and macro analysisunit 450 are functions realized as part of macro analysis jobs submittedto the first computing node 400. The macro analysis unit 450 is anexample of the first analyzing unit 2 a discussed previously in thefirst embodiment of FIG. 1.

The second computing node 500 includes a plurality of micro-analysisunits 510, 520, 530, . . . to analyze the blood flow in medium-scalevessels, arterioles venules, and capillaries. These micro analysis units510, 520, 530, . . . are functions that are realized by a plurality ofprocessor cores in the CPU which execute micro analysis jobs submittedto the second computing node 500.

The third computing node 600 similarly includes a plurality of microanalysis units 610, 620, 630, . . . to analyze the blood flow inmedium-scale vessels, arterioles venules, and capillaries. These microanalysis units 610, 620, 630, . . . are functions that are realized by aplurality of processor cores in the CPU which execute micro analysisjobs submitted to the second computing node 600.

The above micro analyzing units in the computing nodes 500, 600, . . .are an example of the second analyzing units 3 a, 4 a, 5 a, . . .discussed in the first embodiment of FIG. 1. The lines interconnectingthe functional blocks in FIG. 11 are only an example. FIG. 11 omits somecommunication paths for simplicity purposes. The person skilled in theart would appreciate that there may be other communication paths in theactual implementations.

Simulation models stored in the storage device 300 will now be describedin greater detail below. FIG. 12 illustrates an exemplary data structureof a finite element model. This finite element model 310 includes a nodedata table 311 and a mesh data table 312, in which N_(nodes) andN_(mesh) respectively denote the total number of nodes and the totalnumber of meshes.

The node data table 311 is a collection of coordinates that indicate theposition of each node constituting tetrahedral meshes. Each table entryincludes the node number identifying a node and the x, y, and z-axiscoordinate values indicating the node's position in a three-dimensionalvirtual space.

The mesh data table 312 is a set of data that defines the geometry ofmeshes. Each table entry represents a specific tetrahedral meshidentified by its mesh number and defines its shape as a set of nodenumbers indicating which nodes constitute the tetrahedron.

FIG. 13 illustrates an exemplary data structure of a macro model. Thismacro model 320 represents a structure of large coronary arteries andveins as part of the cardiac vessel network. Included in the macro model320 are a node data table 321 and a vessel element data table 322. Thenode data table 321 is similar to the node data table 311 in the finiteelement model 310 of FIG. 12. N_(elem) represents the total number ofvessel elements.

The vessel element data table 322 is formed from the following datafields: element number, node, level, radius, and conductance. Theelement number field contains a number for identifying each specificvessel element. The node field contains the identifiers of two nodes atthe opposite ends of a vessel element. The level field indicates the“level” of a vessel element. Greater level values are given to largervessels. It is capillaries that are classified as the smallest level.The radius field indicates the radius of a vessel, and the conductancefield contains a value indicative of how easily the blood flows throughthe vessel. A higher conductance means that the blood flows moresmoothly with a smaller resistance.

FIG. 14 illustrates an exemplary data structure of a micro model. Thismicro model 330 represents a structure of intermediate vessel network,arterioles, venules, and capillaries as part of the cardiac vesselnetwork. Included in the micro model 330 are a node data table 331 and avessel element data table 332. The node data table 331 is similar to thenode data table 311 in the finite element model 310 of FIG. 12. N_(elem)represents the total number of vessel elements.

The vessel element data table 332 is formed from the following datafields: element number, node, level, length, diameter, vessel wallelasticity, reference pressure difference, and quantity. The elementnumber field contains a number for identifying each specific vesselelement. The node field contains the identifiers of two nodes at theopposite ends of a vessel element. The level field indicates theabove-described level of a vessel element. The length and diameterfields respectively indicate the length and diameter of a vesselelement. The vessel wall elasticity fields contains an elasticitycoefficient of walls of a vessel element. The reference pressuredifference field gives a reference value of pressure difference betweenthe blood pressure within a vessel and the inner pressure of themyocardium. This reference value is used together with a pressuredifference at each simulation time point to calculate the diameter ofthe vessel. The quantity field indicates how many elements are presentin the same level, under the assumption of symmetry.

The foregoing functional blocks of FIG. 11 work in a coordinated way toexecute a coronary circulation simulation, using the data seen in FIGS.12 to 14.

FIG. 15 is a flowchart illustrating an exemplary procedure of a coronarycirculation simulation. Each operation in FIG. 15 is described below inthe order of step numbers.

(Step S101) The user interacts with the CAD system 100 to initiate asimulation. The finite element modeling unit 110 in the CAD system 100produces a finite element model 310 according to the user input. Theproduced finite element model 310 is sent to the managing node 200, andthe managing node 200 stores it in the storage device 300.

(Step S102) The coronary circulation modeling unit 120 in the CAD system100 produces a macro model 320 according to the user input. The producedmacro model 320 is sent to the managing node 200, and the managing node200 stores it in the storage device 300.

(Step S103) The coronary circulation modeling unit 120 produces a micromodel 330 according to the user input. The produced micro model 330 issent to the managing node 200, and the managing node 200 stores it inthe storage device 300.

(Step S104) The execution command unit 210 in the managing node 200submits jobs to a plurality of computing nodes and requests them toexecute a coronary circulation simulation. The computing nodes executethe simulation as requested, by using a plurality of processor cores inparallel.

Each of the above steps of FIG. 15 will now be described in detailbelow.

i) Finite Element Modeling

The CAD system 100 receives medical image data of, for example, theComputed Tomography (CT) or the Magnetic Resonance Image (MRI),Echocardiogram. The finite element modeling unit 110 extracts geometricdata of the heart from the received image and divides the data intomeshes (e.g., tetrahedral or hexahedral meshes) suitable for the finiteelement method.

ii) Coronary Circulation Network Modeling

The modeling process of a coronary circulation network is divided to twoparts, macro modeling and micro modeling. More specifically, vessels maybe mimicked in either a macro model or a micro model depending on theirdimensions. For example, Kassab measured and classified the vesseldiameters into several levels (see, for example, the first to thirdnon-patent documents listed in a previous section). The data of thesediameter levels may be used to assign the vessels to macro and micromodels as follows:

-   -   Macro model to include arteries of level 6 to level 11 and veins        of level −6 to level −12.    -   Micro model to include medium-scale arteries of level 3 to level        5, medium-scale veins of level −3 to level −5, and        microcirculatory vessels of level 3 and below, as well as of        level −3 and above.

Such diameter levels of vessels will be discussed in greater detailbelow. FIG. 16 illustrates exemplary classification of vessels in acoronary circulation model. As seen, one large vessel branches out intosmall vessels. The coronary circulation modeling unit 120 refers to eachsection between the nearest branching points as a “segment.” Thecoronary circulation modeling unit 120 sorts those segments into severallevels based on the diameters of the segments. It is also noted that inthe example of FIG. 16, a series of segments of identical levels isreferred to as an “element.”

The coronary circulation modeling unit 120 starts with a capillary andtraces the branches toward a larger vessel, while assigning anappropriate level to each segment on the way. For example, the coronarycirculation modeling unit 120 classifies segments with diameters of 8 μmor less as capillaries and assigns level 0 to them. The coronarycirculation modeling unit 120 then proceeds from the capillaries towardthe trunk of the tree structure. Suppose, for example, that two vesselsof level N combine together into an upper-scale vessel. The coronarycirculation modeling unit 120 detects this and assigns level N+1 to theupper-scale vessel, where N is a positive integer or zero. In the casewhere two vessels of different levels meet at a point, the coronarycirculation modeling unit 120 determines which segment has a higherlevel and assigns that higher level to the resulting upper-scalesegment. Suppose, for example, that a segment of level N combines with asegment of level N+1. In this case, the subsequent upper-scale segmentis qualified to be level N+1. The coronary circulation modeling unit 120basically follows the above rules when selecting levels, with somemodifications depending on the diameters of segments.

Based on the above measurement, the coronary circulation modeling unit120 further calculates a mean and a standard deviation of the followingquantities for each different level of vessels:

-   -   vessel diameter    -   vessel length    -   segment-to-element ratio (SN)    -   connectivity matrix (ER)

SN means how many segments are successively connected without changingtheir vessel levels.

Connectivity matrix is expressed as ER(M, N), where M is a positiveinteger or zero. ER(M, N) represents the occurrences of branching oflevel N vessels from the nodes on a level M vessel. While capillariesare classified as level 0 by definition, the coronary circulationmodeling unit 120 further subdivides them into four classes C0a, C00,C0v, and Ccc. C0a means capillaries connected to the extreme end ofarteries. C0v means capillaries connected to the extreme end of veins.C00 means capillaries running in parallel with the myocardium. Ccc meanscapillaries running perpendicularly to the longitudinal direction of themyocardium.

iii) Macro Modeling

Upon completion of the vessel classification described above, thecoronary circulation modeling unit 120 produces a macro model fromarteries of level 6 to level 11 and veins of level −6 to level −12.

FIG. 17 is a flowchart illustrating an example of a macro modelingprocess. Each operation in FIG. 17 is described below in the order ofstep numbers.

(Step S111) The coronary circulation modeling unit 120 produces aninitial epicardial coronary vessel model. For example, the coronarycirculation modeling unit 120 first produces an initial version ofepicardial coronary vessel data from given tomographic images of theheart. The coronary circulation modeling unit 120 then maps thisepicardial coronary vessel data on the outer wall of left and rightventricles in the cardiac finite-element model, thereby producing aninitial epicardial coronary vessel model.

(Step S112) The coronary circulation modeling unit 120 puts someadditional data to the initial epicardial coronary vessel model, whichincludes vessel diameters, levels, and presence of branches at eachnode. Here the network model 12 may be configured to display theproduced epicardial coronary vessel model on a monitor screen.

FIG. 18 illustrates an exemplary view of an epicardial coronary vesselmodel. In this example, the illustrated screen 70 for an epicardialcoronary vessel model includes an artery-vs-vein image 71 on the leftand a diameter-specific vessel image 72 on the right. The artery-vs-veinimage 71 visualizes arteries and veins by using different colors. Thediameter-specific vessel image 72 visualizes vessel diameters by usingcolor gradations.

(Step S113) Referring back to FIG. 17, the coronary circulation modelingunit 120 adds septal branches to the epicardial coronary vessel model.Septal branch is the name of some particular vessels of the heart, whichcan be seen in microscope photographs and the like.

FIG. 19 illustrates an example of an epicardial coronary circulationmodel with septal branches. As in the example of FIG. 19, theramification of septal branches from arteries and veins may be seen inmicroscope photographs or anatomical charts. Based on such charts, thecoronary circulation modeling unit 120 produces an epicardial coronarycirculation model 73 having septal branches.

(Step S114) Referring back to FIG. 17, the coronary circulation modelingunit 120 produces blood vessel networks of level 6 or above, as well asthose of level −6 or below, by using some calculation data and randomnumbers.

More specifically, the coronary circulation modeling unit 120 firstdetermines the level of vessels branching out from nodes of the initialepicardial coronary vessels. Think of, for example, a lower-scale vesselbranches out from a vessel of level N. The coronary circulation modelingunit 120 determines the level of this lower-scale vessel by using theconnectivity matrix ER(N, x) and a random number between zero and one.In the case where the original vessel bifurcates at its end, thecoronary circulation modeling unit 120 determines the level of these twolower-scale vessels. The coronary circulation modeling unit 120 thengenerates a random number, assuming that the segment-to-element ratio SNof each level is normally distributed with the mean and standarddeviation calculated from the measurement values. The coronarycirculation modeling unit 120 now determines the number of segmentsconstituting the lower-scale vessel(s) by rounding off the generatedrandom number to the nearest integer.

Similarly to SN, the coronary circulation modeling unit 120 assumes thatthe vessel diameter and length are also normally distributed with theirrespective mean values and standard deviations calculated from themeasurement values. The coronary circulation modeling unit 120determines the vessel diameter by using a random number generatedaccording to the assumed normal distribution. This diameter is appliedthroughout the vessel element in question. The length of each vesselsegment is also determined from a random number generated on the basisof its own statistical distribution. In this way, the coronarycirculation modeling unit 120 produces blood vessel networks.

(Step S115) Lastly, the coronary circulation modeling unit 120 maps theproduced blood vessel network to finite-element meshes of the heartmodel, thus producing a macro model 10 as discussed previously in FIG.2.

iv) Micro Modeling

The coronary circulation modeling unit 120 proceeds to micro modelgeneration. FIG. 20 is a flowchart illustrating an example of a micromodeling process. Each operation in FIG. 20 is described below in theorder of step numbers.

The coronary circulation modeling unit 120 begins the process withproducing a microcirculation model at steps S121 to S123. Thismicrocirculation model represents arterioles of level 3 to level 1,capillaries (vessels of level 0), and venules of level −3 to level −1 ina combined way. The microcirculation model is distinct in its networkstructure, which is unlike the foregoing macro model having a treestructure of bifurcated branches to represent large coronary vessels.For example, the microcirculatory system may be formed between onearteriole and two venules, with a longitudinal size of about 500 μm. Toreproduce such vessels in a model, the coronary circulation modelingunit 120 introduces some additional constraints into the algorithm ofgenerating a tree of vessel branches. More specifically, the coronarycirculation modeling unit 120 produces a network of the microcirculationmodel under the following three conditions:

-   -   Condition 1: Capillaries originated from one arteriole (level 3)        join together into two venules (level −3).    -   Condition 2: Capillaries are about 500 μl in longitudinal size.    -   Condition 3: Some nodes on the capillaries may be connected        directly to each other.

The coronary circulation modeling unit 120 produces a microcirculationmodel by using measurement data and random numbers just as in theforegoing macro modeling method. More specifically, the following threesteps S121 to S123 are executed to produce a microcirculation model.

(Step S121) The coronary circulation modeling unit 120 produces anetwork of level 3 arterial vessels and a network of level −3 venousvessels. For example, the coronary circulation modeling unit 120produces an arterial branch model in a tree structure, based on themeasurement data of Kassab et al. This model includes arterial vesselsbeginning with a level 3 arteriole and ending with level 1 arterioles.Similarly, the coronary circulation modeling unit 120 produces a venousbranch model in a tree structure, beginning with two level −3 venulesand ending with level −1 venules.

(Step S122) The coronary circulation modeling unit 120 producescapillaries running in parallel with the myocardium. For example, thecoronary circulation modeling unit 120 extends the arterial vessels fromtheir extreme ends by repetitively connecting capillary elements untilthe distance between the arterial end and the venous end is reduced toabout 500 μm. Here the diameter and length of each capillary element aredetermined by modifying Kassab's measurement data with some randomnumbers. Because the number of arteries is not equal, in general, tothat of their counterpart veins, the coronary circulation modeling unit120 places additional branches at some capillary nodes to adjust thisdiscrepancy in number, so that the two groups of vessels can be linkedwithout any open ends. This modeling of capillary-level branches is inline with the actual observation of vessels. The coronary circulationmodeling unit 120 determines the number of those branches according tothe above-noted discrepancy in number between arteries and veins,besides stochastically selecting capillary nodes for the branching.

(Step S123) The coronary circulation modeling unit 120 adds some mutualconnections to the capillary nodes. For example, the coronarycirculation modeling unit 120 connects myocardial nodes with each otherby placing additional vessel elements nearly perpendicular to thelongitudinal direction of myocardial cells. Here the coronarycirculation modeling unit 120 uses random numbers to determine thediameter and length of vessel elements as in the foregoing case ofcapillaries running in parallel with myocardial cells, besidesstochastically selecting capillary nodes for the mutual connections.

The above three steps have produced a microcirculation model. FIG. 21gives a specific example illustrating how these steps build amicrocirculation model. At the outset, an arterial vessel network 81 oflevel 3 is produced at step S121, together with a venous vessel network82 of level −3. Then capillaries with a size of about 500 μm areproduced at step S122 to interconnect the arterial and venous vesselnetworks 81 and 82. These capillaries run in parallel with themyocardium and branch out at capillary nodes. At step S123, additionalvessel elements are placed between some myocardial nodes, nearperpendicular to the longitudinal direction of the myocardial cells. Asdepicted in FIG. 21, myocardial cells have a long and narrow shape,about 100 μm in the axial direction (longitudinal direction) and about10 μm in the radial direction. The capillaries are, on the other hand,around 6 μm in diameter.

The microcirculation model built up in this way has a three-dimensionalstructure. Referring back to FIG. 20, S124 and subsequent steps will beexplained below.

(Step S124) The coronary circulation modeling unit 120 produces asymmetric model. For example, this symmetric model extends from each endof the macro model until the microcirculation model is reached, whilebifurcating seven times in that course. More specifically, the arteriesbranch out in the following way: level 5 (single vessel)-->level 5 (twovessels)-->level 5 (four vessels)-->level 4 (eight vessels)-->level 4(16 vessels)-->level 4 (32 vessels)-->level 3 (64 vessels)-->level 3(128 vessels)-->level 3 (reaching the top end of a microcirculationmodel). This similarly applies to the venous side of the symmetric modelby adding a minus sign to the levels.

(Step S125) The coronary circulation modeling unit 120 connectsmicrocirculation models with the symmetric model produced at step S124.The above symmetric model has 128 end points on each of the arterial andvenous sides. The coronary circulation modeling unit 120 thus places 128microcirculation models in parallel such that they are connected to eachend of the macro model via the symmetric model.

As described above, the symmetric model includes three consecutivevessel elements of the same level. That is, there are three level 5arteries, three level 4 arteries, and three level 3 arteries in theabove example. The coronary circulation modeling unit 120 thus makessome fine modifications to the vessel diameters by using, for example,the mean value and standard deviation of each level of vessel elements.Specifically, the initial diameter of each three consecutive vessels isset to the mean value of a particular level of vessels. The modifieddiameters vary from (mean +σ) to (mean), and then to (mean −σ), where σdenotes the given standard deviation of these vessels. For the length ofvessel elements, on the other hand, the coronary circulation modelingunit 120 uses the mean length of each relevant level.

FIG. 22 illustrates an exemplary micro model in which 128microcirculation models are connected in parallel at end points of asymmetrical model. As can be seen from FIG. 22, this micro model 74includes a symmetric model in which the vessels bifurcate seven times inboth the arterial and venous systems. Connected at the resulting endpoints of the symmetric model are 128 microcirculation models. The tablein the right half of FIG. 22 gives the vessel levels, lengths,diameters, and quantities of peer vessels, where the lengths anddiameters are in units of μm.

The above sections have described how the coronary circulation networkmodel is produced. The following sections describe how the proposedmethod executes a simulation. More specifically, the description gives adetailed way of executing a coronary circulation simulation, beginningwith how the number of micro models is determined for each pressure nodeof the myocardium. The description then proceeds to how the myocardiumis coordinated with the microcirculation and explains computationalgorithms for a multi-scale analysis.

(a) Determination of Micro Model Quantity

The density of small blood vessels per unit myocardial volume may varywith their depth in the cardiac walls. Taking this transmurality ofvessel density into consideration, the simulation conditiondetermination unit 130 assigns different relative vessel densities tothe myocardial nodes depending on their depths. More specifically, arelative vessel density of 2.2 is assigned to myocardial nodes close tothe endocardium (inner surface of myocardium) while a relative vesseldensity of 1 is assigned to myocardial nodes close to the epicardium(outer surface of myocardium).

FIG. 23 illustrates the resulting distribution of relative vesseldensity. Specifically, FIG. 23 depicts a cross-section of the heart,with color gradation to represent relative vessel densities. Based onsuch relative vessel densities, the simulation condition determinationunit 130 determines how many micro models to place at each pressurenode.

For example, the execution command unit 210 calculates a micro-modelvessel capacity per unit volume, ρ_(k), at myocardial node k, assumingthat the micro models as a whole have a vessel capacity of 8% of themyocardial volume. The simulation condition determination unit 130 thendetermines the number n_(k) of micro models to be connected to thepressure node k by using equation (1) as in the foregoing firstembodiment.

The simulation condition determination unit 130 also determines otherparameters according to user commands, which include the simulation timestep size, termination conditions, and convergence conditions ofanalysis based on the Newton-Raphson method. The CAD system 100 thentransmits the macro model, micro models, and simulation conditions tothe managing node 200. In response, the managing node 200 submits jobsto a plurality of computing nodes to execute a coronary circulationsimulation in a parallel processing environment.

(b) Equations Relating to Micro Model Vessels

The micro-model vessels are responsible for directly delivering oxygenand nutrition to the myocardium. With their volumetric changes, thevessels accumulate and discharge the blood repetitively. The coronarycirculation simulation mimics not only transport of blood, but also suchaccumulation and discharge actions of the vessels as will be describedbelow.

Generally, a vessel element has different diameters and differentpressures at its opposite ends.

FIG. 24 illustrates a difference of vessel diameters. In thisillustrated model, the vessel element with a conductance of k_(ij) isdivided into two segments with respective conductances of k_(i)(t) andk_(j)(t). Suppose now that these two segments have constant innerpressures of μ_(i)(t) and μ_(i)(t). Then the vessel diameter D_(i)(t) isdetermined from the difference between inner pressures (coronary bloodpressure) and external pressure p_(m)(t), as seen in the followingequation (11). Here the external pressure means pressure of thesurrounding myocardium and corresponds to the pressure value of apressure node of myocardial finite elements.

D _(i)(t)=α·((μ_(i)(t)−p _(m)(t))−(μ_(i) *−p _(m)*))+D _(i)*  (11)

where α represents the elasticity of vessels, and * denotes a referencestate.

For the sake of convenience, values obtained from the Hagen-Poiseuilleflow are used to substitute for the conductance k of segments. Thefollowing equation (12) then gives the conductance k_(ij)(t) of a vesselelement as a harmony mean of k_(i)(t) and k_(j)(t) representingconductances of its segments.

$\begin{matrix}{{k_{ij}(t)} = {\frac{{k_{i}(t)} \cdot {k_{j}(t)}}{{k_{i}(t)} + {k_{j}(t)}} = {\frac{\pi}{64\mspace{14mu} {µL}_{ij}}\frac{{D_{i}(t)}^{4}{D_{j}(t)}^{4}}{{D_{i}(t)}^{4} + {D_{j}(t)}^{4}}}}} & (12)\end{matrix}$

The amount of blood accumulated into segments of a vessel element isrepresented as a time derivative of its volume (as in the second term ofequation (13)). At a vessel node i in the macro model, the flow rateconservation law holds as follows.

$\begin{matrix}{{{\sum\limits_{j \in F_{i}}{{k_{ij}(t)}\left( \mu_{i} \right)}} - \left( \mu_{j} \right) + {\sum\limits_{j \in F_{i}}{\frac{\pi \; L_{ij}}{8}\frac{\left( {D_{i}(t)}^{2} \right)}{t}}}} = 0} & (13)\end{matrix}$

(c) Basic Equations Representing Mixture of Cardiac FEM Model and Blood

The motion of a mixed body of myocardium and blood in themicrocirculatory system is given by the following equation (14).

ρa=∇·(σ^(s) −pI)  (14)

where p represents inner pressure of myocardium, ρ is density of themixed body, a represents acceleration, and σ^(s) is Cauchy stresstensor. The volume conservation law of the mixed body is given by thefollowing equation (15) using volumetric change rate J.

(J−1)−nΔV _(micro)=0  (15)

The second term of equation (15) represents volumetric changes in themicro models, indicating that the volume of the mixed body increases asmuch as the micro models accumulate more blood. ΔV_(Micro) representsthe increase of volume per micro model, relative to the initial state,and n is the number of micro models contained in a unit myocardiumvolume.

There are five unknowns in the above set of equations, three fordisplacement components of the myocardium, one for myocardial pressure,and one for vessel pressure. The relationships among these unknownvariables are expressed as:

$\begin{matrix}{{\begin{pmatrix}A_{mic} & C^{T} & \; & K^{T} \\C & B_{mac} & \; & H^{T} \\\; & \; & D & G^{T} \\K & H & G & F\end{pmatrix}\begin{pmatrix}{\Delta \; \mu_{mic}} \\{\Delta \; \mu_{mac}} \\{\Delta \; \mu} \\{\Delta \; p}\end{pmatrix}} = r} & (16)\end{matrix}$

A_(mic): micro model matrix

B_(mac): macro model matrix

C: interaction between micro model and macro model

D: matrix relating to displacement of myocardium and fluid nodes

F: matrix relating to pressure in myocardium and fluid nodes

K: interaction of pressure between micro model and myocardial nodes

H: interaction of pressure between macro model and fluid nodes

Δμ_(mic): vector relating to pressure at micro model nodes

Δμ_(mac): vector relating to pressure at macro model nodes

Δu: vector relating to displacement of myocardium and fluid nodes

Δp: vector relating to pressure of myocardium and fluid nodes

r: residual vector

The above-noted unknowns can be obtained by solving this matrix equation(16).

(d) Simulation Procedure

The computing nodes 400, 500, 600, . . . run in parallel to execute acoronary circulation simulation in accordance with commands from theexecution command unit 210 in the managing node 200. This parallelprocess may use, for example, the Message-Passing Interface (MPI)standard. The execution command unit 210 assigns a process to oneprocessor core to execute macro model computation. With this process(referred to as “macro procs”), the processor core acts as the macroanalysis unit 450 discussed in FIG. 11.

The execution command unit 210 then assigns more processes to otherprocessor cores to execute micro model computation. Also provided tothese processor cores are parameters specifying, for example, whichmicro models to execute and which pressure nodes to connect micromodels. The assigned processes (referred to as “micro procs”) cause theprocessor cores to function as the micro analysis units 510, 520, . . .discussed in FIG. 11.

The execution command unit 210 further assigns yet another process to aprocessor core to control the entire procedure of a simulation. Thisprocessor core functions as the simulation control unit 440 discussed inFIG. 11. Alternatively, the functions of the simulation control unit 440may be executed together with the macro process by the processor coreserving as the macro analysis unit 450.

FIG. 25 is a flowchart illustrating an exemplary procedure of a coronarycirculation simulation. Steps S131 to S135 in FIG. 25 are executed bythe simulation control unit 440 to control the entire flow. Steps S141to S145 are a micro process executed by the micro analysis units 510,520, and so on. Steps S151 to S155 are a macro process executed by themacro analysis unit 450. The macro process includes analysis ofcontraction and relaxation of the myocardium, as well as the dischargingof intracardiac blood.

This section will describe the entire flow control, micro process, andmacro process individually, according to the step numbers seen in FIG.25. First, the entire flow of the simulation process is controlled asfollows:

(Step S131) The simulation control unit 440 causes each computing node400, 500, 600, . . . to load a finite element model 310 and coronarycirculation model (macro model 320 and micro model 330) of a heart fromthe storage device 300. The computing nodes 400, 500, 600, . . . storethe loaded data in their local memory.

(Step S132) The simulation control unit 440 advances the time step ofthe simulation by one unit increment.

(Step S133) The simulation control unit 440 advances iteration cycles ofthe Newton-Raphson method for nonlinear analysis. For example, thesimulation control unit 440 sends a command to the macro analysis unit450 and micro analysis units 510, 520, . . . to execute the nextNewton-Raphson iteration cycle. This command causes the individual microanalysis units 510, 520, . . . to work in parallel to execute steps S141to S145, as well as the macro analysis unit 450 to execute steps S151 toS155.

(Step S134) The simulation control unit 440 determines whether thenonlinear analysis with the Newton-Raphson method has converged. Thisdetermination may be made by, for example, comparing a new analysisresult with the previous one. If their difference is smaller than orequal to a specified threshold, the simulation control unit 440determines that the solution has been converged and advances the processto step S135. If not, the simulation control unit 440 moves back to stepS133 for the next iteration.

(Step S134) The simulation control unit 440 determines whether thecomputation has been finished for all time steps of the simulation. Thesimulation control unit 440 closes the coronary circulation simulationif it is found that every time step has been done. Otherwise, thesimulation control unit 440 goes back to step S132.

These steps controls the entire process flow of coronary circulationsimulation. During the above course, the micro analysis units 510, 520,. . . execute the following step S141 to S145 for a micro process.

(Step S141) Each of the micro analysis units 510, 520, . . . isassociated with a micro model attached to a particular pressure node.The micro analysis units 510, 520, . . . formulate a micro model matrixA_(Mic) for their corresponding pressure nodes.

(Step S142) Based on their respective matrices A_(Mic) of step S141, themicro analysis units 510, 520, . . . calculate Δμ_(Mic) (with a tilde ontop of μ) according to equation (5).

(Step S143) Each micro analysis unit 510, 520, . . . sends thecalculation result Δμ_(Mic) (with a tilde on top of μ) of step S143 tothe macro analysis unit 450.

(Step S144) The micro analysis units 510, 520, . . . receive from themacro analysis unit 450 a pressure increment Δμ_(Mac) of theircorresponding pressure nodes.

(Step S145) Each micro analysis unit 510, 520, . . . calculates Δμ_(Mic)according to equation (7), using their respective pressure incrementvalues received at step S144.

The above steps are executed for each micro model by the micro analysisunits 510 operating in parallel. The macro analysis unit 450, on theother hand, executes macro process steps S151 to S155 as follows.

(Step S151) The macro analysis unit 450 formulates a macro model matrixB_(Mac).

(Step S152) The macro analysis unit 450 receives Δμ_(Mic) (with a tildeon top of μ) from each micro analysis unit 510, 520, and so on.

(Step S153) The macro analysis unit 450 formulates B_(Mac) (with a tildeon top of B) defined in equation (4).

(Step S154) Based on equation (6), the macro analysis unit 450calculates Δμ_(Mac) at each pressure node.

(Step S155) The macro analysis unit 450 sends Δμ_(Mac) to the microanalysis units 510, 520, . . . , depending on to which pressure nodetheir respective micro models are connected.

These processing operations of FIG. 25 may be presented in a simplifiedway. That is, the simulation control unit 440 first reads a finiteelement model and coronary circulation model as input data for cardiacsimulation. Then at each time step, the macro analysis unit 450 andmicro analysis units 510, 520, . . . perform iterative calculation ofthe Newton-Raphson method for nonlinear analysis. Specifically, themacro analysis unit 450 first formulates a matrix B_(Mac), while themicro analysis units 510, 520, . . . formulate their respective matricesA_(Mic) and solve the equation formed from A_(Mic). The micro analysisunits 510, 520, . . . send their calculation results Δμ_(Mic) (with atilde on top of μ) to the macro analysis unit 450. The macro analysisunit 450 then calculates B_(Mac) (with a tilde on top of B) and solveits equation. The macro analysis unit 450 supplies the micro analysisunits 510, 520, . . . with Δμ_(Mac) for their calculation of Δμ_(Mic).

As can be seen from the above, the second embodiment is configured toexecute parallel calculation of micro models by assigning a microprocess to a plurality of CPU cores serving as micro analysis units 510.The second embodiment enables concurrent execution of the micro andmacro processes at least in part and thus permits the parallel computingsystem to exert its performance. The second embodiment also takesadvantage of a symmetric model, which reduces the data size of micromodels by the factor of 128 compared with the case without symmetry.

The efficient use of a parallel computing system contributes torealization of a coronary circulation simulation that mimics even ablood flow in cardiac capillaries. This feature of the second embodimentmakes it possible to investigate the behavior of a heart in itsmicrocirculatory system, as opposed to conventional simulation methods.The scope of the simulation may be expanded to include delivery ofchemical compounds and interaction between capillaries and muscletissue. This expansion helps researchers to estimate possible effects orside effects of a new drug in development.

(C) Third Embodiment

This section describes a third embodiment which combines another type ofsimulation with the blood flow simulation of the first or secondembodiment. This additional simulation enables analysis of metabolism ofmyocardial cells. Metabolism is a series of chemical reactions in whichthe myocardial cells produce adenosine triphosphate (ATP) from oxygenand nutrition obtained from the capillary blood flow. For mathematicalmodeling of metabolism, see the fifth non-patent document. The combinedsimulation of blood flow and myocardial metabolism only becomes possiblewith the proposed capillary-level blood flow simulation.

To implement a simulation for analyzing metabolism of myocardial cells,the third embodiment uses two additional equations (17) and (18),together with the foregoing equations discussed in the first or secondembodiment.

$\begin{matrix}{\frac{\partial C}{\partial t} = {{{- v}\frac{\partial C}{\partial x}} - {{\nabla{\cdot D}}{\nabla C}}}} & (17)\end{matrix}$

Equation (17) describes the concentration C of oxygen and nutritiondelivered by the coronary blood flow, where v is the blood velocity andD represents diffusion coefficients.

$\begin{matrix}{\frac{\partial C}{\partial t} = {{{{- \nabla} \cdot K}{\nabla C}} + q}} & (18)\end{matrix}$

where C is the concentration of oxygen and nutrients, K representsdiffusion coefficients, and q indicates metabolic consumption of oxygenand nutrients by the myocardial cells. Equation (18) describes movementof oxygen and nutrition which is caused by the difference inconcentration between the blood and myocardial cells.

The third embodiment solves the given equations including (17) and (18)by using the same method discussed in the first or second embodiment.Equation (17) applies to both the macro model and micro models. Equation(18) applies to myocardial finite elements having a micro model and amyocardial pressure node connecting that micro model. In this way, thethird embodiment enables simulation of blood flow in combination withthe metabolism of myocardial cells.

(D) Fourth Embodiment

This section describes a fourth embodiment which proposes a method forreducing the amount of computation relative to the foregoing thirdembodiment.

The metabolism is affected by the coronary blood flow, but its changesmay not come to the surface until the heart beats ten or more times. Theparallel computer may, however, consume a lot of time for computation ofthese ten heartbeats, depending on the performance of the parallelcomputer used. The fourth embodiment is thus directed to reduction ofcomputational load in cardiac simulation. The second and fourthembodiments use the same system configuration discussed previously inFIGS. 8 to 11. Accordingly this section describes the fourth embodimentby using the same components illustrated in FIGS. 8 to 11.

FIG. 26 is a flowchart illustrating an exemplary simulation procedureaccording to the fourth embodiment. Each operation in FIG. 26 isdescribed below in the order of step numbers.

(Step S201) The simulation control unit 440 causes each computing node400, 500, 600, . . . to load input data from the storage device 300,including a finite element model 310 and coronary circulation model(macro model 320 and micro model 330) of a heart, as well as data foranalysis of metabolism of myocardial cells. The computing nodes 400,500, 600, . . . store the loaded data in their local memory.

(Step S202) The simulation control unit 440 advances the time step ofthe simulation by one unit increment.

(Step S203) The macro analysis unit 450 works in cooperation with themicro analysis units 510, 520, . . . to simulate one heartbeat.Calculated at this step S203 are motion of the myocardium, dischargingof intracardiac blood, flow of coronary circulation, delivery of oxygenand nutrition, and metabolism. The macro analysis unit 450 and the microanalysis units 510, 520, . . . store the resulting data of coronarycirculation flow in their respective local memory.

(Step S204) The macro analysis unit 450 works in cooperation with themicro analysis units 510, 520, . . . to simulate ten heartbeats.Calculated at this step S204 are delivery of oxygen and nutrition andmetabolism.

(Step S205) The simulation control unit 440 determines whether thecomputation has been finished for all time steps of the simulation. Thesimulation control unit 440 closes the coronary circulation simulationif it is found that every time step has been done. Otherwise, thesimulation control unit 440 goes back to step S202.

As can be seen from the above steps, the proposed method of the fourthembodiment first simulates a single heartbeat to analyze the motion ofmyocardium, discharging of intracardiac blood, flow of coronarycirculation, delivery of oxygen and nutrition, and metabolism. At eachtime step in this single heartbeat duration, the obtained data of flowrates in the coronary circulation is saved. Then with the saved flowrate data, the proposed method executes computation of ten moreheartbeats, now focusing on the delivery of oxygen and nutrients and themetabolism. In this way, the proposed method reduces the amount ofcomputational load in simulating ten or more heartbeats and thus enablesthe user to analyze possible variations in the metabolism in a practicalsimulation time, without the need for a faster parallel computer.

(E) Fifth Embodiment

This section describes a fifth embodiment which uses features of thefirst to fourth embodiments to evaluate effects of coronary arterystenosis.

FIG. 27 illustrates an exemplary system configuration according to thefifth embodiment. This section will focus on its distinct features,while affixing like reference numerals to like elements discussed in thesecond embodiment. Besides having the same system elements of the secondembodiment in FIG. 8, the fifth embodiment in FIG. 27 further includes astorage device 700, a network 91, and a terminal device 800.

The additional storage device 700 is linked to one network 62 for use bythe computing nodes 400, 500, 600, . . . to save data of their analysisresults. The storage device 700 is also accessible to a remote terminaldevice 800 via another network 91. This terminal device 800 is used toevaluate the effect of coronary artery stenosis based on the analysisresults stored in the storage device 700.

For example, the user produces data of a coronary circulation model byusing a CAD system 100 from medical images obtained from a ComputedTomography (CT) or Magnetic Resonance Imaging (MRI) system (notillustrated). This coronary circulation model reflects some constrictedportions of the patient's coronary arteries. Or the produced coronarycirculation model may have a couple of arteries with some amount ofstenosis that is set by the user to represent a future risk of narrowingor blockage of the coronary arteries.

With the model data produced above, a plurality of computing nodes 400,500, 600, simulate the coronary circulation according to the foregoingfirst to fourth embodiments. The user can receive the result from thesimulation system via his or her terminal device 800. The terminaldevice 800 executes evaluation of the analysis result in terms of howthe stenosis of coronary arteries affects the motion of the myocardium,discharging of intracardiac blood, flow of coronary circulation,delivery of oxygen and nutrition, and metabolism of the heart inquestion.

(F) Other Embodiments and Variations

The first to fifth embodiments have been described above in the contextof coronary circulation simulation. The proposed simulation method maysimilarly be applied to hemodynamics simulation of other organs.

The system according to the second to fifth embodiments has also beendescribed as being formed from a plurality of apparatuses. It ispossible, however, to integrate any two or more of those apparatusesinto a single apparatus. For example, the foregoing CAD system 100 andmanaging node 200 may be implemented in a single computer.

The data processing operations of the second to fifth embodiment areencoded in software programs for execution by CPU cores. The programmedcode may partly be implemented in the form of electronic circuit. Forexample, a part of the foregoing processing functions may be implementedby using one or more digital signal processors (DSP), applicationspecific integrated circuits (ASIC), programmable logic devices (PLD),and the like.

Various embodiments and their variations have been described above.According to one aspect of the embodiments, the proposed method andapparatus efficiently simulate the dynamics of blood circulation,including all or some of the capillary-level analysis of blood pressuredrop, evaluation of a heart with a particular microcirculationstructure, and combined analysis of metabolism and microcirculation.

All examples and conditional language provided herein are intended forthe pedagogical purposes of aiding the reader in understanding theinvention and the concepts contributed by the inventor to further theart, and are not to be construed as limitations to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although one or more embodiments of thepresent invention have been described in detail, it should be understoodthat various changes, substitutions, and alterations could be madehereto without departing from the spirit and scope of the invention.

What is claimed is:
 1. A simulation method comprising: storing a geometric model representing a shape of an organ as a collection of elements formed from a plurality of nodes and a plurality of connections among the nodes; storing a first vessel network model representing a network of first vessels whose diameters are larger than or equal to a specified threshold; storing a plurality of second vessel networks each representing a network of second vessels whose diameters are smaller than the specified threshold, wherein one or more of the second vessel networks are connected to each of the nodes; first analyzing, by a processor, hemodynamics in the first vessels belonging to the first vessel network model, based on the geometric model of the organ and reflecting motion of the organ; and second analyzing, by the processor, hemodynamics in the second vessels belonging to the second vessel network models connected to the plurality of nodes, by using result data of the first analyzing which indicates the hemodynamics in the first vessels at each of the nodes.
 2. The simulation method according to claim 1, wherein: the second vessel network model includes capillaries and intermediate-layer vessels placed at opposite ends of the capillaries to connect the capillaries with the first vessels defined in the first vessel network model; and the intermediate-layer vessels are divided into arterial vessels and venous vessels which are symmetrically arranged with respect to the capillaries.
 3. The simulation method according to claim 1, wherein a larger number of second vessel network models are connected to a node with a higher vessel density, so as to reflect an anatomical feature of the organ.
 4. The simulation method according to claim 1, wherein the result data of the first analyzing includes data about blood pressure at the nodes for use by the second analyzing.
 5. The simulation method according to claim 1, wherein: the organ under simulation is a heart; and the motion of the organ is caused by contraction and relaxation of myocardium of the heart.
 6. The simulation method according to claim 5, wherein a larger number of second vessel network models are placed at the nodes on an endocardial side of a wall of the heart than at the nodes on an epicardial side of the wall of the heart.
 7. The simulation method according to claim 1, wherein: the processor is provided in plurality; and the second analyzing is executed by the plurality of processors to analyze the hemodynamics in the second vessels belonging to the second vessel network models connected at the plurality of nodes.
 8. A simulator apparatus comprising a processor configured to perform a procedure including: storing a geometric model representing a shape of an organ as a collection of elements formed from a plurality of nodes and a plurality of connections among the nodes; storing a first vessel network model representing a network of first vessels whose diameters are larger than or equal to a specified threshold; storing a plurality of second vessel networks each representing a network of second vessels whose diameters are smaller than the specified threshold, wherein one or more of the second vessel networks are connected to each of the nodes; first analyzing hemodynamics in the first vessels belonging to the first vessel network model, based on the geometric model of the organ and reflecting motion of the organ; and second analyzing hemodynamics in the second vessels belonging to the second vessel network models connected to the plurality of nodes, by using result data of the first analyzing which indicates the hemodynamics in the first vessels at each of the nodes.
 9. A computer-readable storage medium storing a program for execution by a computer including a plurality of processing devices, the program causing the computer to perform a procedure comprising: storing a geometric model representing a shape of an organ as a collection of elements formed from a plurality of nodes and a plurality of connections among the nodes; storing a first vessel network model representing a network of first vessels whose diameters are larger than or equal to a specified threshold; storing a plurality of second vessel networks each representing a network of second vessels whose diameters are smaller than the specified threshold, wherein one or more of the second vessel networks are connected to each of the nodes; first analyzing hemodynamics in the first vessels belonging to the first vessel network model, based on the geometric model of the organ and reflecting motion of the organ; and second analyzing hemodynamics in the second vessels belonging to the second vessel network models connected to the plurality of nodes, by using result data of the first analyzing which indicates the hemodynamics in the first vessels at each of the nodes, wherein the plurality of processing devices in the computer perform computation of the second vessel network models in parallel. 